Why do payers refuse to fund "effective" therapies?
Bibliographic record
Abstract
6 Background: The pan-Canadian Oncology Drug Review (pCODR) provides funding recommendations to the provinces of Canada on Health Canada approved systemic therapies. Why would they recommend against funding drugs that meet regulatory standards? Methods: 136 reviews for 70 compounds were completed by pCODR from inception to January 1, 2019 were collected and data abstracted using a standardized form. For all submissions that received a negative recommendation, the Clinical Guidance Panel (CGP), Economic Guidance Panel (EGP), and Expert Review Committee (pERC) reports were examined to determine the underlying cause for rejection. Results: 32 (24%) of submissions received a negative recommendation. In all cases, this was due to concerns regarding the clinical effectiveness of the compound. Economic considerations were never cited as rational for rejection. 10 (32%) were submitted with supporting evidence from non-comparative phase 2 clinical data, and this level of evidence was cited as the basis for the recommendation. 4 (12%) supported by randomized phase 2 evidence were rejected in support of awaiting phase 3 trials in progress. 6 phase 2-based recommendations have since been overturned on re-submission with phase 3 data. Of the 18 (56%) rejected with phase 3 data, 4 had demonstrated an overall survival advantage over standard of care (range 1.5-3.8 months) that was deemed not clinically significant. 12 were rejected due to lack of confidence in surrogate end-points employed in the trial. 1 was rejected due to diminishing clinical benefit observed over time, and 1 was rejected due to data quality. The EGP calculated the median cost-effectiveness of the proposed indications to be $188,537/QALY (range 22,326-2,120,433), with a median cost increase of $47,529 (2,536-226,752) and life-years gained of 3.8 months (0.8-11). Conclusions: Regulatory approval does not translate to clinically meaningful effectiveness. Bodies that consider opportunity cost may have a higher threshold for minimum benefit than regulators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.177 | 0.530 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".